Causal Inference Methods for Policy Evaluation

00-Introduction

Jacopo Mazza

Utrecht School of Economics

2025

Introductions

About me

About you

  • Name
  • Where are you from?
  • What is your background?
  • One other thing about you

Course Outline

Assumed Background

What I will not assume:

  • Prior knowledge of causal inference

What I will assume:

  • Basic knowledge of statistics:
    • Hypothesis testing
    • Regression analysis
    • Basic probability theory
    • Basic calculus
  • Basic knowledge of econometrics:
    • OLS
  • Some knowledge of R

Learning Outcomes

  1. Develop identification strategies to uncover causal effects for a given research question and in a given context.
  2. Applying state-of-the-art causal inference techniques using real-world data and statistical software.
  3. Critically evaluate the assumptions and limitations of various causal inference methods.
  4. Apply visualization techniques and effectively communicate and interpret research findings.

Textbook and computing

Format

  • Monday 1:30 hours of lecture (13:15–15:00) – BBG 219
  • Tuesday 1:30 hours of lab (15:15–17) – RUPPERT 002
    • KBG 224 on the 4th of March

Instructors

Jacopo Mazza, PhD
Utrecht University
School of Economics
Adam Smith Building 112.A
Email: j.mazza@uu.nl

Sönke Matthewes, PhD
Utrecht University
School of Economics
Adam Smith Building 112.A
Email: s.matthewes@uu.nl

Assessment

  • Week 6: Midterm exam (50%).
    • March 10th. 17:30-19:30. EDUC - ALPHA
  • Week 10: Final exam (50%)
    • April 7th. 13:30-15:30. OLYMPOS - HAL 1

Format: 3 questions, 2:00 hours.

Tentative Schedule

Weeks Topic Chapter Mixtape
2 Potential outcome causal model 3
3 Matching and subclassification 5
4 Instrumental variables (IV) 7
5 Regression Discontinuity 6
6 Midterm Exam Monday 10/3
7 Difference-in-differences 1 8 and 9
8 Difference-in-differences 2 9
9 Synthetic Control 10
10 Final Exam Monday 7/4

Course Policy

  • Attendance is not mandatory, but highly recommended.
  • Electronic devices are not allowed1:
  • We have good evidence that they hurt your learning.